WO2022065154A1

WO2022065154A1 - Information processing device, information processing method, and program

Info

Publication number: WO2022065154A1
Application number: PCT/JP2021/033904
Authority: WO
Inventors: 規高田; 秀生鶴; 哲也諏訪; 翔平大段; 隆幸菅原
Original assignee: 株式会社Ｊｖｃケンウッド
Priority date: 2020-09-24
Filing date: 2021-09-15
Publication date: 2022-03-31
Also published as: EP4202820A1; CN116194914A; US20230222884A1

Abstract

An information processing device (10) comprising: a behavior state detection sensor (20) that detects behavior state information pertaining to the behavior state of a user; a behavior pattern information generation unit (42) that, on the basis of the behavior state information, generates behavior pattern information in a multidimensional space having at least the parameters of time/date, place, and length of time that the behavior state was detected as axes, and groups the behavior pattern information in each space in which the density of a behavior pattern information group of clustered behavior pattern information surpasses a prescribed density; a behavior score calculation unit (44) that calculates, as a behavior score, information pertaining to the size of a space that includes a behavior pattern information group; and a behavior pattern identification unit (46) that identifies a behavior pattern information group that exists in a space for which the behavior score value is a prescribed value or greater as a behavior pattern of the user.

Description

Information processing equipment, information processing methods, and programs

This disclosure relates to information processing devices, information processing methods, and programs.

There is known a technique of detecting a user's movement and identifying the user's behavior by using a wearable device worn by the user.

For example, Patent Document 1 describes a mobile phone device that detects user acceleration information and controls an operation mode using the detected acceleration information. Patent Document 2 describes a motion information measurement system that measures motion information of a desired portion of a user and recognizes a motion state of the whole body.

Japanese Patent Application Laid-Open No. 2003-46630 Japanese Unexamined Patent Publication No. 2004-184351

Here, it is required to specify the user's behavior pattern based on the information regarding the user's behavior state.

It is an object of the present disclosure to provide an information processing device, an information processing method, and a program capable of specifying a user's behavior pattern based on information on the user's behavior state.

The information processing apparatus according to one aspect of the present disclosure includes a behavioral state detection sensor that detects behavioral state information related to the user's behavioral state, and at least the date, time, place, and time when the behavioral state is detected based on the behavioral state information. A behavior pattern information generation unit that generates behavior pattern information in a multidimensional space with the parameter of The behavior score calculation unit that calculates information about the size of the space including the behavior pattern information group as the behavior score, and the behavior pattern information group in which the value of the behavior score is equal to or higher than a predetermined value are the behaviors of the user. It is provided with an action pattern specifying unit that is specified as a pattern.

The information processing apparatus according to one aspect of the present disclosure is based on a behavioral state sensor that detects behavioral state information regarding the user's behavioral state, a biological sensor that detects biological information regarding the user's biological information, and the biological information. It includes an autonomic nerve activity calculation unit that calculates the autonomic nerve activity of the user, and an output control unit that changes the intensity of output from the output unit according to the intensity of the autonomic nerve activity.

The information processing apparatus according to one aspect of the present disclosure is based on a behavioral state sensor that detects behavioral state information regarding the user's behavioral state, a biological sensor that detects biological information regarding the user's biological information, and the biological information. An autonomic nerve activity calculation unit that calculates the autonomic nerve activity of the user, and an autonomic nerve activity correction unit that corrects the autonomic nerve activity based on the country or region in which the behavior pattern of the user is specified. To prepare for.

The information processing method according to one aspect of the present disclosure includes a step of detecting a behavioral state information regarding a user's behavioral state, and at least parameters of a date, time, place, and time when the behavioral state is detected based on the behavioral state information. A step of generating behavior pattern information in a multidimensional space as a coordinate axis and grouping each behavior pattern information group in which the density of the behavior pattern information group exceeds a predetermined density, and a space including the behavior pattern information group. A step of calculating an action score that calculates information about the size of the action score as an action score, and a step of specifying the action pattern information group that exists in the space having a value of the action score of a predetermined value or more as the action pattern of the user. include.

The information processing method according to one aspect of the present disclosure includes a step of detecting behavioral state information regarding the user's behavioral state, a step of detecting biological information regarding the user's biological information, and a step of detecting the user's biological information based on the biological information. It includes a step of calculating the autonomic nerve activity and a step of changing the intensity of the output from the output unit according to the intensity of the autonomic nerve activity.

The information processing method according to one aspect of the present disclosure includes a step of detecting behavioral state information regarding the user's behavioral state, a step of detecting biological information regarding the user's biological information, and a step of detecting the user's biological information based on the biological information. It includes a step of calculating the autonomic nerve activity and a step of correcting the autonomic nerve activity based on the country or region in which the behavior pattern of the user is specified.

The program according to one aspect of the present disclosure includes a step of detecting behavioral state information regarding a user's behavioral state, and at least the date and time, place, and time parameters at which the behavioral state is detected as coordinate axes based on the behavioral state information. A step of generating behavior pattern information in a multidimensional space and grouping the behavior pattern information groups into groups in which the density of the behavior pattern information group exceeds a predetermined density, and the size of the space including the behavior pattern information group. A step of calculating an action score that calculates information about the behavior as an action score, a step of specifying the action pattern information group in which the value of the action score is equal to or higher than a predetermined value in the space, and a step of specifying the action pattern of the user. Have the computer execute the step of storing the action pattern in the storage unit.

The program according to one aspect of the present disclosure includes a step of detecting behavioral state information regarding the user's behavioral state, a step of detecting biological information regarding the user's biological information, and the user's autonomic nerve based on the biological information. A computer is made to execute a step of calculating the activity and a step of changing the intensity of the output from the output unit according to the intensity of the autonomic nerve activity.

The program according to one aspect of the present disclosure includes a step of detecting behavioral state information regarding the user's behavioral state, a step of detecting biological information regarding the user's biological information, and the autonomic nerve of the user based on the biological information. Have the computer perform a step of calculating the activity and a step of correcting the autonomic nervous activity based on the country or region in which the user's behavior pattern is specified.

According to the present disclosure, the behavior pattern of the user can be specified based on the information regarding the behavioral state of the user.

FIG. 1 is a schematic diagram schematically showing an information processing apparatus according to the first embodiment. FIG. 2 is a block diagram showing a configuration example of the information processing apparatus according to the first embodiment. FIG. 3 is a flowchart showing an example of the processing flow of the information processing apparatus according to the first embodiment. FIG. 4 is a diagram for explaining a multidimensional space that generates behavior pattern information. FIG. 5 is a diagram for explaining a format for storing an action pattern. FIG. 6 is a flowchart showing an example of the processing flow of the information processing apparatus according to the second embodiment. FIG. 7 is a diagram for explaining daily behavior and extraordinary behavior. FIG. 8 is a block diagram showing a configuration example of the information processing apparatus according to the third embodiment. FIG. 9 is a flowchart showing an example of the processing flow of the information processing apparatus according to the third embodiment. FIG. 10 is a graph showing an example of a pulse wave. FIG. 11 is a diagram for explaining a format for storing an action pattern. FIG. 12 is a block diagram showing a configuration example of the information processing apparatus according to the fourth embodiment. FIG. 13 is a diagram for explaining an example of correction data. FIG. 14 is a flowchart showing an example of the processing flow of the information processing apparatus according to the fourth embodiment. FIG. 15 is a diagram for explaining an example of correction data. FIG. 16 is a flowchart showing an example of the processing flow of the information processing apparatus according to the fifth embodiment. FIG. 17 is a block diagram showing a configuration example of the information processing apparatus according to the sixth embodiment. FIG. 18A is a diagram for explaining an example of user history data. FIG. 18B is a diagram for explaining an example of user history data. FIG. 18C is a diagram for explaining an example of user history data. FIG. 19 is a flowchart showing an example of the flow of the learning process according to the sixth embodiment. FIG. 20 is a flowchart showing an example of the processing flow of the information processing apparatus according to the sixth embodiment. FIG. 21 is a diagram for explaining a configuration example of the information processing system according to the seventh embodiment. FIG. 22 is a block diagram showing a configuration example of the server device according to the seventh embodiment. FIG. 23 is a flowchart showing an example of the processing flow of the server device according to the seventh embodiment. FIG. 24 is a flowchart showing an example of the flow of the learning process according to the seventh embodiment. FIG. 25 is a flowchart showing an example of the processing flow of the information processing apparatus according to the seventh embodiment. FIG. 26 is a block diagram showing a configuration example of the information processing apparatus according to the eighth embodiment. FIG. 27A is a diagram for explaining a method of relatively displaying the temporal transition of the activity score. FIG. 27B is a diagram for explaining a method of relatively displaying the temporal transition of the activity score. FIG. 27C is a diagram for explaining a method of relatively displaying the temporal transition of the activity score. FIG. 27D is a diagram for explaining a method of relatively displaying the temporal transition of the activity score. FIG. 27E is a diagram for explaining a method of relatively displaying the temporal transition of the activity score. FIG. 28 is a diagram for explaining a method of outputting information showing the temporal transition of the activity score.

Hereinafter, embodiments according to the present disclosure will be described in detail with reference to the attached drawings. It should be noted that the present disclosure is not limited to this embodiment, and when there are a plurality of embodiments, the present embodiment also includes a combination of the respective embodiments. Further, in the following embodiments, the same parts are designated by the same reference numerals, so that duplicate description will be omitted.

[First Embodiment]
FIG. 1 is a schematic diagram of an information processing apparatus according to the first embodiment. As shown in FIG. 1, the information processing apparatus 10 is a so-called wearable device worn on the body of the user U. In the example of the present embodiment, the information processing device 10 includes a device 10A worn on the eyes of the user U, a device 10B worn on the ears of the user U, and a device 10C worn on the arm of the user U. The device 10A attached to the eyes of the user U includes a display unit 26A described later that outputs a visual stimulus to the user U (displays an image), and the device 10B attached to the ear of the user U gives an auditory stimulus to the user U. The device 10C attached to the arm of the user U includes a later-described audio output unit 26B that outputs (voice), and includes a later-described tactile stimulus output unit 26C that outputs a tactile stimulus to the user U. However, the configuration of FIG. 1 is an example, and the number of devices and the mounting position on the user U may be arbitrary. For example, the information processing device 10 is not limited to a wearable device, and may be a device carried by the user U, for example, a so-called smartphone or tablet terminal.

FIG. 2 is a block diagram showing a configuration example of the information processing apparatus according to the first embodiment. As shown in FIG. 2, the information processing apparatus 10 includes an action state sensor 20, an input unit 22, an output unit 24, a communication unit 26, a storage unit 28, and a control unit 30.

The behavioral state sensor 20 is a sensor that detects behavioral state information regarding the behavioral state of the user U wearing the information processing device 10. The behavior state information of the user U may include various information regarding the behavior of the user U. The action state information of the user U may include at least information regarding the physical movement of the user U, the date and time of the action, the place of the action, and the time of the action.

The behavioral state sensor 20 includes a camera 20A, a microphone 20B, a GNSS receiver 20C, an acceleration sensor 20D, a gyro sensor 20E, an optical sensor 20F, a temperature sensor 20G, and a humidity sensor 20H. However, the behavioral state sensor 20 may include an arbitrary sensor for detecting behavioral state information, for example, a camera 20A, a microphone 20B, a GNSS receiver 20C, an acceleration sensor 20D, and a gyro sensor 20E. , The optical sensor 20F, the temperature sensor 20G, and the humidity sensor 20H may be included, or may include other sensors.

The camera 20A is an image pickup device, and captures the periphery of the information processing device 10 by detecting visible light around the information processing device 10 (user U) as behavioral state information. The camera 20A may be a video camera that captures images at predetermined frame rates. The position and orientation of the camera 20A in the information processing apparatus 10 are arbitrary, but for example, the camera 20A is provided in the apparatus 10A shown in FIG. 1 and the imaging direction is the direction in which the face of the user U is facing. It may be there. As a result, the camera 20A can image an object in the line of sight of the user U, that is, an object within the field of view of the user U. Further, the number of cameras 20A is arbitrary, and may be singular or plural. If there are a plurality of cameras 20A, the information in the direction in which the cameras 20A are facing is also acquired.

The microphone 20B is a microphone that detects voice (sound wave information) around the information processing device 10 (user U) as behavioral state information. The position, orientation, number, and the like of the microphone 20B provided in the information processing apparatus 10 are arbitrary. If there are a plurality of microphones 20B, information in the direction in which the microphones 20B are facing is also acquired.

The GNSS receiver 20C is a device that detects the position information of the information processing device 10 (user U) as the action state information. The position information here is the earth coordinates. In the present embodiment, the GNSS receiver 20C is a so-called GNSS (Global Navigation Satellite System) module, which receives radio waves from satellites and detects the position information of the information processing device 10 (user U).

The acceleration sensor 20D is a sensor that detects the acceleration of the information processing device 10 (user U) as behavioral state information, and detects, for example, gravity, vibration, and impact.

The gyro sensor 20E is a sensor that detects the rotation and orientation of the information processing device 10 (user U) as behavioral state information, and detects it using the principles of Coriolis force, Euler force, centrifugal force, and the like.

The optical sensor 20F is a sensor that detects the intensity of light around the information processing device 10 (user U) as behavioral state information. The optical sensor 20F can detect the intensity of visible light, infrared rays, and ultraviolet rays.

The temperature sensor 20G is a sensor that detects the temperature around the information processing device 10 (user U) as behavioral state information.

The humidity sensor 20H is a sensor that detects the humidity around the information processing device 10 (user U) as behavioral state information.

The input unit 22 is a device that accepts user operations, and may be, for example, a touch panel.

The output unit 24 outputs the output result of the information processing device 10. The output unit 24 includes, for example, a display unit 24A for displaying an image and an audio output unit 24B for outputting audio. In the present embodiment, the display unit 24A is, for example, a so-called HMD (Head Mounted Display). The audio output unit 24B is a speaker that outputs audio.

The communication unit 26 is a module that communicates with an external device or the like, and may include, for example, an antenna or the like. The communication method by the communication unit 26 is wireless communication in this embodiment, but the communication method may be arbitrary.

The storage unit 28 is a memory that stores various information such as calculation contents and programs of the control unit 30, and is, for example, a RAM (Random Access Memory), a main storage device such as a ROM (Read Only Memory), and an HDD (HDD). Includes at least one of external storage devices such as Hard Disk Drive).

The learning model 28A and the map data 28B are stored in the storage unit 28. The learning model 28A is an AI model used to specify the environment in which the user U is located based on the environment information. The map data 28B is data including position information of existing buildings and natural objects, and can be said to be data in which the earth coordinates and actual buildings and natural objects are associated with each other. The processing using the learning model 28A, the map data 28B, and the like will be described later. The learning model 28A, the map data 28B, and the program for the control unit 30 stored by the storage unit 28 may be stored in a recording medium readable by the information processing device 10. Further, the program for the control unit 30 stored by the storage unit 28, the learning model 28A, and the map data 28B are not limited to being stored in advance in the storage unit 28, and information is used when using these data. The processing device 10 may acquire from an external device by communication.

The control unit 30 controls the operation of each unit of the information processing device 10. The control unit 30 is realized by, for example, using a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like to execute a program stored in a storage unit (not shown) using a RAM or the like as a work area. The control unit 30 may be realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 30 may be realized by a combination of hardware and software.

The control unit 30 includes an action state information acquisition unit 40, an action pattern information generation unit 42, an action score calculation unit 44, an action pattern specifying unit 46, a memory control unit 48, and an output control unit 50.

The behavior state information acquisition unit 40 controls the behavior state sensor 20 to cause the behavior state sensor 20 to detect the behavior state information of the user U. The behavior state information acquisition unit 40 acquires the behavior state information detected by the behavior state sensor 20.

The behavior pattern information generation unit 42 generates behavior pattern information based on the behavior status information acquired by the behavior status information acquisition unit 40. The behavior pattern information generation unit 42 generates behavior pattern information in a multidimensional space whose coordinate axes are at least the date and time, place, and time parameters when the behavior state of the user U is detected, based on the behavior state information, for example.

The behavior score calculation unit 44 calculates the behavior score based on the behavior pattern information generated by the behavior pattern information generation unit 42. The behavior score calculation unit 44, for example, groups the behavior pattern information groups in which the behavioral state information is collected for each space in which the density exceeds a predetermined density. The behavior score calculation unit 44 calculates the behavior score of the behavior pattern information group based on the space including the grouped behavior pattern information group. Specifically, the behavior score calculation unit 44 calculates, for example, information regarding the size of the space including the behavior pattern information group as the behavior score.

The behavior pattern specifying unit 46 specifies the behavior pattern of the user U based on the behavior score calculated by the behavior score calculation unit 44. The behavior pattern specifying unit 46 determines that the behavior corresponding to the behavior pattern information group whose behavior score value exceeds a predetermined threshold value is the behavior pattern of the user U. The behavior pattern specifying unit 46 is based on, for example, image data, voice data, position information, acceleration information, attitude information, infrared and ultraviolet intensity information, temperature information, humidity information, etc. acquired by the behavior state information acquisition unit 40. , Identify the type of action the user U was performing.

The memory control unit 48 controls the storage unit 28 to perform storage. Information about the behavior pattern of the user U specified by the behavior pattern specifying unit 46 is stored in the storage unit 28. The storage control unit 48 stores information about the behavior pattern of the user U specified by the behavior pattern specifying unit 46 in the storage unit 28 in a predetermined format. The predetermined format will be described later.

The output control unit 50 controls the output unit 24 to output. The output control unit 50 controls, for example, the display unit 24A to display information regarding an action pattern. The output control unit 50 controls, for example, the voice output unit 24B to output information regarding the behavior pattern by voice.

[Processing content]
The processing content of the information processing apparatus 10 according to the first embodiment will be described with reference to FIG. FIG. 3 is a flowchart showing an example of the processing flow of the information processing apparatus 10 according to the first embodiment.

The control unit 30 acquires the behavior state information regarding the behavior of the user U from the behavior state sensor 20 (step S10). Specifically, the behavior state information acquisition unit 40 acquires image data obtained by capturing the periphery of the information processing apparatus 10 (user U) from the camera 20A. The behavior state information acquisition unit 40 acquires voice data obtained by collecting voices around the information processing device 10 (user U) from the microphone 20B. The behavior state information acquisition unit 40 acquires the position information of the information processing device 10 (user U) from the GNSS receiver 20C. The behavior state information acquisition unit 40 acquires the acceleration information of the information processing device 10 (user U) from the acceleration sensor 20D. The behavior state information acquisition unit 40 acquires the posture information of the information processing device 10 (user U) from the gyro sensor 20E. The behavior state information acquisition unit 40 acquires the intensity information of infrared rays and ultraviolet rays around the information processing apparatus 10 (user U) from the optical sensor 20F. The behavior state information acquisition unit 40 acquires temperature information around the information processing device 10 (user U) from the temperature sensor 20G. The behavior state information acquisition unit 40 acquires humidity information around the information processing device 10 (user U) from the humidity sensor 20H. The behavior state information acquisition unit 40 sequentially acquires such information at predetermined intervals. The action state information acquisition unit 40 may acquire each action state information at the same timing or at different timings. Further, the predetermined period until the next behavioral state information is acquired may be arbitrarily set, and the predetermined period may be the same or different for each environmental information.

The action of the user U may include three elements: the physical movement of the body of the action itself, the date and time when the action is performed, the place where the action is performed, and the time when the action is performed at the place. The behavior of the user U may include not only the movement of the body of the user U but also actions such as "playing golf", "watching a movie", and "shopping". Even if the body movement of the user U acquired by the action state information acquisition unit 40 is the same, if the position information is different, the action performed may be different.

The control unit 30 generates the behavior pattern information of the user U (step S11). Specifically, the behavior pattern information generation unit 42 generates the behavior pattern information of the user U based on the behavior state information of the user U acquired by the behavior state information acquisition unit 40. The control unit 30 groups the behavior pattern information group (step S12). Specifically, the behavior pattern information generation unit 42 groups each space in which the density of the behavior pattern information group in which the behavior pattern information is collected exceeds a predetermined density. The control unit 30 calculates the behavior score (step S13). Specifically, the behavior score calculation unit 44 calculates the distance from the center to the end of the space including the behavior pattern information group as the behavior score. In other words, the behavior score calculation unit 44 calculates the size of the space including the behavior pattern information group as the behavior score.

FIG. 4 is a diagram for explaining a multidimensional space that generates behavior pattern information.

FIG. 4 shows a three-dimensional space whose coordinate axes are date and time, place, and time. In FIG. 4, the date and time is from 0:00 to 24:00, the place is a one-dimensional linear distance from the home, and the time is the time from the start of the action to the end of the action, but it is limited to this. do not have. For example, the location may be a name, an address, or the like.

The behavior pattern information generation unit 42 generates behavior pattern information by plotting points P at predetermined time intervals in the three-dimensional space shown in FIG. The behavior pattern information generation unit 42 plots the point P at intervals of 1 minute, for example, but the present invention is not limited to this. It is assumed that the behavior pattern information generation unit 42 is watching a movie for "2 hours" in "around area A2" at "around noon", for example. In this case, the behavior pattern information generation unit 42 points at predetermined intervals from the intersection of "around noon", "around area A2" and "2 hours" to "around 14:00" parallel to the date and time axis. Plot P. It should be noted that the fact that the user U was in the movie theater means that the behavior pattern specifying unit 46 has image data, audio data, position information, acceleration information, attitude information, infrared and ultraviolet intensity information, temperature information, and information around the user U. It can be specified based on humidity information and the like. In the case of "purchasing groceries" as the action of the user U, there may usually be multiple candidates for grocery stores such as supermarkets and underground shopping malls of department stores.

The behavior pattern information generation unit 42 groups in the three-dimensional space shown in FIG. 4 as the same behavior pattern in a space where the density of the behavior pattern information group generated as behavior pattern information and gathering points P exceeds a predetermined density. To become. The behavior pattern information generation unit 42 scans the space S in which the behavior pattern information can be generated, for example, by using the unit space US. The behavior pattern information generation unit 42 counts, for example, the number of points P included in the unit space US at each location in the space S. The behavior pattern information generation unit 42 specifies, for example, a location having the largest number of points P included in the unit space US as the center of the behavior pattern information. The action pattern information generation unit 42 counts the number of points P included in the unit space US around the unit space US having the largest number of points P, and is about 60% of the unit space US having the largest number of points P. Up to the unit space US including the point P is specified as the same group G. The action score calculation unit 44 calculates, for example, the length of the perpendicular line drawn from the center of the group G to any surface as the action score. The action score calculation unit 44 calculates, for example, the volume of the group G as the action score. The behavior pattern information generation unit 42 may specify a plurality of groups G in the three-dimensional space shown in FIG.

Here, the behavior of the user U includes, for example, "going to the dealer" for "purchasing a car" and "going to the home center" for "purchasing a chair", which are not frequently purchased. There can also be behavior to buy. It is considered that these actions are plotted separately in place, time, and date and time in the three-dimensional space shown in FIG. However, for example, if User U does "gymnastics" for "about 10 minutes" at a nearby "park" at 6 am every day, the points plotted in the three-dimensional space will be crowded in the space day by day. Come on. Even within the same park location, the place where you do gymnastics may vary from day to day, and the time may vary from 10 minutes to ± 2 minutes. Therefore, the behavior pattern information generation unit 42 can plot and generate behavior pattern information so as to be biased like a relatively large dense group in a three-dimensional space. In this way, by selecting parameters such as time, place, and time, which are relatively simple information, as the axis, it becomes possible to treat a dense group of three-dimensional space with specific fluctuations as one action pattern. ..

In the example shown in FIG. 4, the behavior pattern of the user U is shown in a three-dimensional space, but the present disclosure is not limited to this. In the present embodiment, the behavior pattern of the user U may be generated in any multidimensional space. For example, the place is "straight line distance from home", but the place may be "latitude" and "longitude". In this case, the space in which the behavior pattern information is generated is a four-dimensional space. Further, although the date and time are set to "0:00 to 24:00" to indicate one day, an axis having a scalar value of 7 units may be added as the "day of the week" to form a five-dimensional space. In this case, for example, when the user U goes to work from Monday to Friday and Saturday and Sunday are holidays, the plot of the behavior pattern information group may change significantly between Monday and Friday and Saturday and Sunday. From the viewpoint of the axis of "day of the week", it becomes easy to distinguish between the behavior pattern of daily behavior and the behavior pattern of extraordinary behavior. The behavior patterns of daily behavior and extraordinary behavior will be described later.

The time is the time from the start to the end of the action, but this disclosure is not limited to this. For example, if the behavior is an intermittent continuous event such as "200 swings of the bat" or "how many steps (or ran)", the frequency may be used. For example, if the user U has a habit of exercising regularly, by changing the time to frequency, all behavior patterns can be grasped as a parameter of "exercise" called "movement", which is interesting data for user U. It is more likely that it can be displayed.

Return to Fig. 3. The control unit 30 determines whether or not the behavior score is less than the threshold value (step S14). Specifically, the behavior pattern specifying unit 46 determines whether or not the behavior score calculated by the behavior score calculation unit 44 in step S13 is less than a predetermined threshold value. If it is determined that the value is less than the threshold value (step S14; Yes), the process proceeds to step S15. If it is determined that the value is not less than the threshold value (step S14; No), the process proceeds to step S18.

If it is determined to be Yes in step S14, the control unit 30 specifies an action pattern (step S15). Specifically, the behavior pattern specifying unit 46 identifies the behavior corresponding to the behavior pattern information group whose behavior score is equal to or less than a predetermined threshold value as the behavior pattern of the user U.

The control unit 30 identifies the type of behavioral state of the specified behavioral pattern (step S16). Specifically, the behavior pattern specifying unit 46 may identify the type of behavior state performed by the user U based on the behavior state information acquired by the behavior state information acquisition unit 40.

More specifically, the behavior pattern specifying unit 46 may specify the behavior state of the user U by using, for example, the learning model 28A. The learning model 28A is constructed by learning and constructing a plurality of data sets as one data set, with the detection result of the behavior state sensor 20 and the information indicating the type of the behavior state indicated by the detection result of the behavior state sensor 20 as one data set. It is an AI (Artificial Intelligence) model. The behavior pattern specifying unit 46 inputs the detection result of the behavior state sensor 20 into the learned learning model 28A, acquires the information indicating the type of the behavior state indicated by the detection result, and obtains the information indicating the type of the behavior state indicated by the detection result. Identify the type. The behavior pattern specifying unit 46 uses the learned learning model 28A to specify, for example, that the user U is playing golf, shopping, being in a movie theater, or the like.

The control unit 30 stores the action pattern in the storage unit 28 (step S17). Specifically, the memory control unit 48 records the behavior pattern specified in step S16 in a predetermined format.

FIG. 5 is a diagram for explaining a format for storing an action pattern. In the present embodiment, the behavior pattern can be associated with, for example, a place where 16 to 64 types of behavior can be predicted in advance. For example, a "golf course" can predict "playing golf", a "cinema" can predict "watching a movie", and a "shopping center" can predict "shopping". It can be predicted that "doing", and if it is "park", it can be predicted that "doing gymnastics". In this embodiment, since the position information of the user U can be acquired from the GNSS receiver 20C of the behavior state sensor 20, the place visited by the user U can be registered for several weeks. Thereby, in the present embodiment, the candidates for the behavior pattern related to the lifestyle of the user U can be numbered in order.

As shown in FIG. 5, the storage format F1 may include an area D1, an area D2, an area D3, an area D4, and an area D5.

In the area D1, a numerical value numbered with the specified behavior pattern is stored. The area D1 is composed of, for example, 3 bytes. In the area D2, the number of dimensions of the space in which the behavior pattern information is plotted as a group is stored. The area D2 is composed of, for example, 1 byte. In this case, the space can be up to 255 dimensions. In the area D3, the behavior score R of the behavior pattern information group determined to be the behavior pattern of the user U is stored. The behavior score R has fluctuations in the judgment error of the behavior pattern, and the smaller the value of the behavior score R, the higher the reliability of the behavior pattern. The area D4 is a reserve area. In the area D5, which is the last bit of the last 1-byte area of the reserve area, an identifier indicating whether the behavior pattern is everyday or extraordinary is stored. In the area D5, 0 is described in the case of the behavior pattern of daily behavior described later, and 1 is described in the case of the behavior pattern of extraordinary behavior. The reserve area can be used when ancillary information is desired to be added when each behavior pattern occurs. The reserve area has, for example, an area of 6 bytes or more, and each numerical information corresponding to an N-dimensional (N is an arbitrary integer) dimension may be described.

Return to Fig. 3. The control unit 30 determines whether or not there is another group (step S18). Specifically, the behavior score calculation unit 44 determines whether or not there is a grouped behavior pattern information group for which the behavior score should be calculated. If it is determined that there is another group (step S18; Yes), the process proceeds to step S13. If it is determined that there is no other group (step S18; No), the process proceeds to step S19.

The control unit 30 determines whether or not to end the process (step S19). Specifically, the control unit 30 determines that the process is terminated when it receives an operation to end the process, or when it receives an operation to turn off the power. If it is determined that the process is not completed (step S19; No), the process proceeds to step S10. When it is determined to end the process (step S19; Yes), the process of FIG. 3 is terminated.

As described above, the information processing apparatus 10 according to the first embodiment detects the behavioral state of the user U and generates an behavioral pattern information group according to the behavioral state in a multidimensional space. Thereby, the information processing apparatus 10 according to the first embodiment can specify the behavior pattern of the user U based on the behavior pattern information group.

[Second Embodiment]
Next, the second embodiment will be described. FIG. 6 is a flowchart showing an example of the processing flow of the information processing apparatus according to the second embodiment. Since the configuration of the information processing apparatus according to the second embodiment is the same as that of the information processing apparatus 10 shown in FIG. 2, the description thereof will be omitted.

In the second embodiment, the information processing apparatus 10 determines whether the identified user U's behavior pattern is a daily daily behavior or an extraordinary extraordinary behavior.

FIG. 7 is a diagram for explaining daily behavior and extraordinary behavior.

In FIG. 7, the space SA shows, for example, a range grouped as the same behavior pattern. The space SB has, for example, the same center as the space SA, and its volume is about 60% of the space SA.

In the second embodiment, the behavior pattern information included in the space SB is a behavior pattern in which the behavior score is less than the first threshold value. In the second embodiment, the behavior pattern whose behavior score is less than the first threshold value is determined to be the behavior pattern of daily behavior. In the example shown in FIG. 7, the behavior pattern corresponding to the point P1 in the space SB is determined to be the behavior pattern of daily behavior.

In the second embodiment, the behavior pattern included in the space between the space SA and the space SB is a behavior pattern in which the behavior score is equal to or more than the first threshold and less than the second threshold. In the second embodiment, the behavior pattern having the behavior score of the first threshold value or more and less than the second threshold value is determined to be the behavior pattern of the extraordinary behavior. In the example shown in FIG. 7, the behavior pattern corresponding to the point P2 in the space between the space SA and the space SB is determined to be the behavior pattern of extraordinary behavior.

In the second embodiment, the behavior pattern included in the space outside the space SA is an behavior pattern in which the behavior score is equal to or higher than the second threshold value. In the second embodiment, the behavior pattern whose behavior pattern is equal to or higher than the second threshold value is excluded from the target and is not included as the behavior pattern of the user.

Return to Fig. 6. Since the processing of steps S20 to S23 is the same as the processing of steps S10 to S13 shown in FIG. 3, the description thereof will be omitted.

The control unit 30 determines whether or not the behavior score is less than the first threshold value (step S24). Specifically, the behavior pattern specifying unit 46 determines whether or not the behavior score calculated by the behavior score calculation unit 44 in step S23 is less than a predetermined first threshold value. If it is determined that the threshold value is less than the first threshold value (step S24; Yes), the process proceeds to step S25. If it is determined that the threshold value is not less than the first threshold value (step S24; No), the process proceeds to step S28.

If it is determined to be Yes in step S24, the control unit 30 specifies an action pattern of daily actions (step S25). Specifically, the behavior pattern specifying unit 46 identifies the behavior corresponding to the behavior pattern information group whose behavior score is less than a predetermined first threshold value as the behavior pattern of the daily behavior of the user U.

The control unit 30 identifies the type of behavioral state of the behavioral pattern of the specified daily behavior (step S26). Specifically, the behavior pattern specifying unit 46 may identify the type of the behavior state of the daily activity performed by the user U based on the behavior state information acquired by the behavior state information acquisition unit 40.

The control unit 30 stores the behavior pattern of daily activities in the storage unit 28 (step S27). Specifically, the memory control unit 48 records the behavior pattern of the daily behavior specified in step S25 in a predetermined format.

If No is determined in step S24, the control unit 30 determines whether or not the behavior score is equal to or greater than the first threshold and less than the second threshold (step S28). Specifically, the behavior pattern specifying unit 46 determines whether or not the behavior score calculated by the behavior score calculation unit 44 in step S23 is equal to or greater than a predetermined first threshold value and less than the second threshold value. If it is determined that the threshold value is equal to or greater than the first threshold value and is less than the second threshold value (step S28; Yes), the process proceeds to step S29. If it is determined that it is not the first threshold value or more and less than the second threshold value (step S28; No), the process proceeds to step S32.

If it is determined to be Yes in step S28, the control unit 30 specifies an action pattern of extraordinary behavior (step S29). Specifically, the behavior pattern specifying unit 46 determines that the behavior corresponding to the behavior pattern information group whose behavior score is equal to or more than a predetermined first threshold value and less than the second threshold value is an extraordinary behavior behavior pattern.

The control unit 30 identifies the type of behavioral state of the behavioral pattern of the specified extraordinary behavior (step S30). Specifically, the behavior pattern specifying unit 46 may identify the type of behavioral state of the extraordinary behavior performed by the user U based on the behavioral state information acquired by the behavioral state information acquisition unit 40.

The control unit 30 stores the behavior pattern of daily activities in the storage unit 28 (step S31). Specifically, the memory control unit 48 records the behavior pattern of the extraordinary behavior specified in step S25 in a predetermined format.

Since the processes of step S32 and step 33 are the same as the processes of step S18 and step S19 shown in FIG. 3, the description thereof will be omitted.

As described above, the information processing apparatus 10 according to the second embodiment determines whether the behavior pattern is a daily behavior or an extraordinary behavior based on the behavior score. Thereby, the information processing apparatus 10 according to the second embodiment can determine whether the same action is a daily routine or a new action.

Specifically, in the second embodiment, the discrimination between daily behavior and extraordinary behavior is, for example, whether the behavior pattern that occurred during commuting time on weekdays is a routine behavior or was the behavior pattern intentionally performed. It can be used to judge whether or not you are interested in such things. For example, if you commute from one station to another at a fixed time every day, the behavior of walking toward the station is routine if it is the same time, and it is not an active behavior that you are interested in. Statistically, the behavior pattern data can be ignored for calculations.

[Third Embodiment]
Next, the information processing apparatus according to the third embodiment will be described with reference to FIG. FIG. 8 is a block diagram showing a configuration example of the information processing apparatus according to the third embodiment.

As shown in FIG. 8, the information processing apparatus 10a includes the biometric sensor 32 and the control unit 30a includes the biometric information acquisition unit 52 and the activity score calculation unit 54. Is different.

User U's behavior is accompanied by biometric information such as the degree of excitement as well as physical movement. Therefore, when specifying the behavior pattern of the user U, it is preferable to consider the psychological situation of the user U at the time of the behavior. The information processing apparatus 10a calculates an activity score indicating the degree to which the user U enjoys the action.

The biosensor 32 is a sensor that detects the biometric information of the user U. The biosensor 32 may be provided at any position as long as it can detect the biometric information of the user U. The biometric information here is not immutable such as a fingerprint, but is preferably information whose value changes according to the state of the user U, for example. Furthermore, it is preferable that the biometric information here is information about the autonomic nerve of the user U, that is, information whose value changes regardless of the intention of the user U. Specifically, the biological sensor 32 includes the pulse wave sensor 32A and detects the pulse wave of the user U as biological information. The biosensor 32 may include an electroencephalogram sensor that detects the electroencephalogram of the user U.

The pulse wave sensor 32A is a sensor that detects the pulse wave of the user U. The pulse wave sensor 32A may be, for example, a transmissive photoelectric sensor including a light emitting unit and a light receiving unit. In this case, the pulse wave sensor 32A is configured such that, for example, the light emitting portion and the light receiving portion face each other with the fingertip of the user U interposed therebetween, and the light receiving portion receives the light transmitted through the fingertip, and the pressure of the pulse wave. The pulse waveform may be measured by utilizing the fact that the larger the value, the larger the blood flow. However, the pulse wave sensor 32A is not limited to this, and may be any method capable of detecting a pulse wave.

The biometric information acquisition unit 52 controls the biometric sensor 32 to cause the biometric sensor 32 to detect biometric information. The biological information acquisition unit 52 acquires the biological information detected by the biological sensor 32.

The activity score calculation unit 54 calculates the autonomic nerve activity level based on the biometric information acquired by the biometric information. The method of calculating the autonomic nerve activity will be described later. The activity score calculation unit 54 calculates the activity score. The activity score calculation unit 54 calculates the activity score based on the behavior score calculated by the behavior score calculation unit 44, the behavior pattern specified by the behavior pattern specifying unit 46, and the autonomic nerve activity level.

[Processing content]
The processing content of the information processing apparatus 10a according to the third embodiment will be described with reference to FIG. 9. FIG. 9 is a flowchart showing an example of the processing flow of the information processing apparatus 10a according to the third embodiment.

Since the process of step S40 is the same as the process of step S10 shown in FIG. 3, the description thereof will be omitted.

The control unit 30a acquires the biometric information of the user U (step S41). Specifically, the biological information acquisition unit 52 controls the pulse wave sensor 32A of the biological sensor 32 to acquire the pulse wave information of the user U. In the present embodiment, as will be described later, the autonomic nervous activity degree that serves as a guideline for indicating the degree of stress, relaxation, interest, and concentration in the psychological aspect of the user U is determined by using the pulse wave information of the user U. calculate.

Since the processes of steps S42 to S47 are the same as the processes of steps S12 to S17 shown in FIG. 3, the description thereof will be omitted.

The control unit 30a calculates the activity score (step S48). Specifically, the activity score calculation unit 54 calculates the activity score of the user U based on the pulse wave information acquired in step S41.

The pulse wave will be described with reference to FIG. FIG. 10 is a graph showing an example of a pulse wave. As shown in FIG. 10, the pulse wave is a waveform in which a peak called R wave WR appears at predetermined time intervals. The pulse beat is caused by spontaneous combustion of pacemaker cells in the sinus node of the heart. The pulse rhythm is strongly influenced by both the sympathetic and parasympathetic nerves. Sympathetic nerves promote cardiac activity. Parasympathetic nerves suppress cardiac activity. Normally, the sympathetic nerve and the parasympathetic nerve act in opposition to each other. At rest or near, the parasympathetic nerve becomes dominant. Normally, pulse rate increases when sympathetic excitement secretes adrenaline and decreases when parasympathetic excitement secretes acetylcholine. Therefore, it is useful to examine the fluctuation of the RR interval in the electrocardiogram for the function test of the autonomic nerve. This includes "normal reference values and standard prediction formulas for autonomic nerve function tests using fluctuations in the ECG RR interval" by Fujimoto et al. (Autonomic Nervous System, Vol. 30, No. 2, 1987, pp. 167-173) and Hayano et al. "Heart rate variability and autonomic nervous function" (Biophysics, 28-4, P32-36, 1988). As shown in FIG. 10, the RR interval is an interval between R wave WRs that are continuous in a time series. Heart rate variability is measured with the R wave, which is the apex of the QPS wave of the signal waveform, as one pulse. The fluctuation of the R wave interval of the electrocardiogram, that is, the fluctuation of the time interval of the RR interval indicating the R wave interval of FIG. 10 is used as an autonomic nerve index. The validity of using the time interval fluctuation of the RR interval as an autonomic nerve index has been reported in many medical institutions. Fluctuations in the RR interval increase at rest and decrease during stress.

There are some characteristic fluctuations in the fluctuation of the RR interval. One is a low-frequency component that appears near 0.1 Hz, which is derived from the modulation of sympathetic nervous system activity associated with the feedback regulation of blood vessel blood pressure. The other is respiratory-tuned modulation, a high-frequency component that reflects respiratory sinus arrhythmia. The high-frequency component reflects the direct interference of the vagal nerve anterior neuron by the respiratory center and the antireceptor reflexes of lung extension receptors and changes in blood pressure due to respiration, and is regarded as a parasympathetic index that mainly affects the heart. That is, among the waveform components obtained by measuring the fluctuation between the RR waves of the pulse wave, the power spectrum of the low frequency component indicates the activity of the sympathetic nerve, and the power spectrum of the high frequency component indicates the activity of the parasympathetic nerve. I can say.

The fluctuation of the input pulse wave is obtained by the differential value of the RR interval value. In this case, if the differential value of the RR interval is not the data at equal intervals in time, the activity score calculation unit 54 converts the data into time series data at equal intervals by using three-dimensional spline interpolation or the like. The activity score calculation unit 54 performs orthogonal transformation of the differential value of the RR interval by fast Fourier transform or the like. As a result, the activity score calculation unit 54 calculates the power spectrum of the high frequency component of the differential value of the RR interval value of the pulse wave and the power spectrum of the low frequency component. The activity score calculation unit 54 calculates the sum of the power spectra of the high frequency components as RRHF. The activity score calculation unit 54 calculates it as RRLF, which is the sum of the power spectra of the low frequency components. The activity score calculation unit 54 calculates the autonomic nerve activity using the formula (1). The activity score calculation unit 54 may be referred to as an autonomic nerve activity calculation unit.

In the formula (1), AN is the autonomic nervous activity, RRHF is the sum of the power spectra of the high frequency components, and RRLF is the sum of the power spectra of the low frequency components. C1 and C2 are fixed values defined in order to suppress the divergence of the solution of AN.

The activity score calculation unit 54 calculates the activity score using the formula (2).

In equation (2), NS is the activity score, AP is the behavior pattern, R is the behavior score, and AN is the autonomic nerve activity. That is, the activity score calculation unit 54 may calculate the activity score by using a function including the behavior pattern, the behavior score, and the autonomic nerve activity as parameters.

Further, the activity score calculation unit 54 may calculate the activity score by using, for example, a learning model. The learning model is an AI model constructed by learning a behavioral pattern, a behavioral score, an autonomic nervous activity, and an activity score as one data set, and learning a plurality of data sets as teacher data. In this case, the activity score calculation unit 54 inputs the behavior pattern, the behavior score, and the autonomic nerve activity into the learned learning model, acquires the information indicating the activity score, and obtains the activity score. calculate.

The control unit 30a presents the activity score to the user U (step S49). Specifically, the output control unit 50 controls at least one of the display unit 24A and the voice output unit 24B, and presents the activity score to the user U.

The control unit 30a stores the behavior pattern and the activity score in the storage unit 28 (step S50). Specifically, the memory control unit 48 records the behavior pattern and the activity score identified in step S46 in a predetermined format.

FIG. 11 is a diagram for explaining a format for storing an action pattern. As shown in FIG. 11, the storage format F2 may include a region D1a, a region D2a, a region D3a, a region D4a, a region D5a, and a region D6a.

In the area D1a, a numerical value numbered with the specified behavior pattern is stored. The region D1a is composed of, for example, 3 bytes. In the area D2a, the number of dimensions of the space in which the behavior pattern information is plotted as a group is stored. The area D2a is composed of, for example, 1 byte. In the area D3a, the behavior score R of the behavior pattern information group determined to be the behavior pattern of the user U is stored. The autonomic nerve activity is stored in the region D4a. The region D4a is composed of, for example, 2 bytes. The activity score is stored in the region D5a. The region D5a is composed of, for example, 2 bytes. The region D6a is a reserve region.

Since the processes of steps S51 and S52 are the same as the processes of steps S18 and S19 shown in FIG. 3, respectively, the description thereof will be omitted.

As described above, the information processing apparatus 10a according to the third embodiment calculates an activity score indicating the degree to which the user enjoys the action when the action specified as the action pattern of the user U is performed. be able to. Thereby, the information processing apparatus 10a according to the third embodiment can more appropriately specify the behavior pattern of the user U.

[Fourth Embodiment]
Next, the information processing apparatus according to the fourth embodiment will be described with reference to FIG. FIG. 12 is a block diagram showing a configuration example of the information processing apparatus according to the fourth embodiment.

As shown in FIG. 12, the information processing apparatus 10b is an information processing apparatus shown in FIG. 8 in that the storage unit 28a stores the correction data 28C and the control unit 30b includes the activity score correction unit 56. It is different from 10a.

If the country or region changes, even if the behavior pattern is the same, the feeling may differ due to differences in sensibilities. The information processing apparatus 10b corrects the activity score according to the country or region.

The correction data 28C is data used by the activity score correction unit 56 to correct the activity score. The correction data 28C is data in which, for example, an action pattern and a correction coefficient for multiplying an activity score according to a country or region are associated with each other.

FIG. 13 is a diagram for explaining an example of correction data. FIG. 13 shows an action pattern MP1, an action pattern MP2, and an action pattern MP3 as action patterns. Further, as a country or region, area A1, area A2, and area A3 are shown. In FIG. 13, the behavior pattern is conceptually shown as the behavior pattern MP1 or the like, but is actually shown concretely as “playing golf” or the like. Further, although the country or region is conceptually shown as area A1, in reality, a specific country name such as Japan or a specific region name such as Tokyo is shown.

As shown in FIG. 13, in the area A1, the activity score of the behavior pattern MP1 is multiplied by 0.5, the activity score of the behavior pattern MP2 is multiplied by 0.2, and the activity score of the behavior pattern MP3 is multiplied by 0.2. Multiply by 0.1. In area A2, the activity score of the behavior pattern MP1 is multiplied by 0.2, the activity score of the behavior pattern MP2 is multiplied by 0.6, and the activity score of the behavior pattern MP3 is multiplied by 0.9. In area A3, the activity score of the behavior pattern MP1 is multiplied by 0.3, the activity score of the behavior pattern MP2 is multiplied by 0.7, and the activity score of the behavior pattern MP3 is multiplied by 0.5. As shown in FIG. 13, different countries or regions can have different correction factors for multiplying the activity score, even if the behavior patterns are the same. The correction coefficient is generated, for example, by conducting a questionnaire survey on behavior patterns that can be assumed in advance for each country or region. In FIG. 13, the correction coefficients are classified for each area as in areas A1 to A3, but the correction coefficients may be classified under predetermined conditions other than the area. For example, depending on the type of behavior pattern to be targeted, the correction coefficient may be classified according to age, gender, or the like.

The activity score correction unit 56 corrects the activity score calculated by the activity score calculation unit 54. Specifically, the activity score correction unit 56 corrects the activity score using the correction data 28C based on the country or region in which the behavior pattern is specified.

[Processing content]
The processing content of the information processing apparatus 10b according to the fourth embodiment will be described with reference to FIG. FIG. 14 is a flowchart showing an example of the processing flow of the information processing apparatus 10b according to the fourth embodiment.

Since the processes of steps S60 to S68 are the same as the processes of steps S40 to S48 shown in FIG. 9, the description thereof will be omitted.

The control unit 30b corrects the activity score (step S69). Specifically, the activity score correction unit 56 corrects the activity score calculated by the activity score calculation unit 54 using the correction data 28C based on the position information specified by the behavior pattern identification unit 46. ..

The control unit 30b presents the corrected activity score to the user U (step S70). Specifically, the output control unit 50 controls at least one of the display unit 24A and the voice output unit 24B, and presents the corrected activity score to the user U.

The control unit 30b stores the behavior pattern and the corrected activity score in the storage unit 28 (step S71). Specifically, the memory control unit 48 records the behavior pattern specified in step S66 and the corrected activity score in a predetermined format.

Since the processes of steps S72 and S73 are the same as the processes of steps S51 and S52 shown in FIG. 9, respectively, the description thereof will be omitted.

As described above, the information processing apparatus 10b according to the fourth embodiment corrects the activity score by multiplying the activity score by a correction coefficient according to the country or region. Thereby, the information processing apparatus 10b according to the fourth embodiment can more appropriately correct the activity score according to the country or region.

[Modified example of the fourth embodiment]
Next, a modified example of the fourth embodiment will be described. In the fourth embodiment, as shown in FIG. 13, the activity score is corrected by using the correction data 28C in which the behavior pattern and the area are associated with each other. However, the correction data does not have to be associated with an action pattern. That is, a dedicated correction coefficient may be set for each area. In this case, the activity score correction unit 56 may correct the autonomic nerve activity calculated by the activity score calculation unit 54 based on the correction data. In this case, the activity score correction unit 56 may also be referred to as an autonomic nerve activity correction unit.

FIG. 15 is a diagram for explaining an example of correction data. As shown in FIG. 15, in the correction data 28Ca, a correction coefficient is determined for each area. In the example shown in FIG. 15, the correction coefficient of the area A1 is 0.5, the correction coefficient of the area A2 is 0.2, and the correction coefficient of the area A3 is 0.3.

For example, when it is determined that the area where the autonomic nerve activity of the user U is calculated is the area A1, the activity score correction unit 56 autonomously multiplies the calculated autonomic nerve activity by 0.5. Corrects nerve activity. Thereby, in the modified example of the fourth embodiment, the activity of the autonomic nerve can be calculated more appropriately according to each region.

[Fifth Embodiment]
Next, the fifth embodiment will be described. FIG. 16 is a flowchart showing an example of the processing flow of the information processing apparatus according to the fifth embodiment. Since the configuration of the information processing apparatus according to the fifth embodiment is the same as that of the information processing apparatus 10b shown in FIG. 12, the description thereof will be omitted.

In the fifth embodiment, the information processing apparatus 10b independently calculates the activity score for the behavior pattern of the specified daily behavior of the user U and the behavior pattern of the extraordinary behavior. Further, in the fifth embodiment, the information processing apparatus 10b independently corrects the calculated activity score of the behavior pattern of the daily behavior of the user U and the activity score of the behavior pattern of the extraordinary behavior.

Since the processes of steps S80 to S84 are the same as the processes of steps S40 to S44 shown in FIG. 9, the description thereof will be omitted.

Since the processes of steps S85 to S87 are the same as the processes of steps S24 to S26 shown in FIG. 6, the description thereof will be omitted.

The control unit 30b calculates the activity score of the behavior pattern of daily behavior (step S88). Specifically, the activity score calculation unit 54 calculates the activity score of the behavior pattern of the daily behavior of the user U based on the pulse wave information acquired in step S81.

The control unit 30b corrects the activity score of the behavior pattern of daily behavior (step S89). Specifically, the activity score correction unit 56 uses the correction data 28C based on the position information specified by the behavior pattern identification unit 46 to determine the behavior pattern of daily behavior calculated by the activity score calculation unit 54. Correct the activity score.

The control unit 30b presents the user U with the activity score of the behavior pattern of the corrected daily behavior (step S90). Specifically, the output control unit 50 controls at least one of the display unit 24A and the voice output unit 24B, and presents the corrected activity score to the user U.

The control unit 30b stores the behavior pattern of daily activities and the corrected activity score in the storage unit 28 (step S91). Specifically, the memory control unit 48 records the behavior pattern of the daily behavior identified in step S86 and the corrected activity score in a predetermined format.

If No is determined in step S85, the process proceeds to step S92. Since the processes of steps S92 to S94 are the same as the processes of steps S28 to S30 shown in FIG. 6, the description thereof will be omitted.

The control unit 30b calculates the activity score of the behavior pattern of the extraordinary behavior (step S95). Specifically, the activity score calculation unit 54 calculates the activity score of the behavior pattern of the user U's extraordinary behavior based on the pulse wave information acquired in step S81.

The control unit 30b corrects the activity score of the behavior pattern of extraordinary behavior (step S96). Specifically, the activity score correction unit 56 uses the correction data 28C based on the position information specified by the behavior pattern identification unit 46 to calculate the behavior pattern of extraordinary behavior calculated by the activity score calculation unit 54. Correct the activity score of.

The control unit 30b presents the corrected activity score of the behavior pattern of the extraordinary behavior to the user U (step S97). Specifically, the output control unit 50 controls at least one of the display unit 24A and the voice output unit 24B, and presents the corrected activity score to the user U.

The control unit 30b stores the behavior pattern of extraordinary behavior and the corrected activity score in the storage unit 28 (step S91). Specifically, the memory control unit 48 records the behavior pattern of the extraordinary behavior identified in step S86 and the corrected activity score in a predetermined format.

Since the processes of steps S99 and S100 are the same as the processes of steps S51 and S52 shown in FIG. 9, respectively, the description thereof will be omitted.

As described above, the information processing apparatus 10b according to the fifth embodiment independently calculates the behavior pattern of daily behavior and the activity score when performing the behavior specified as the behavior pattern of extraordinary behavior. Can be done. Thereby, the information processing apparatus 10b according to the fifth embodiment can more appropriately specify the behavior pattern of the user U.

Further, the information processing apparatus 10b according to the fifth embodiment multiplies the activity score by a correction coefficient according to the country or region to obtain the activity score of the behavior pattern of daily behavior and the activity of the behavior pattern of extraordinary behavior. Correct the degree score. Thereby, the information processing apparatus 10b according to the fifth embodiment can more appropriately correct the activity score according to the country or region.

[Sixth Embodiment]
Next, the information processing apparatus according to the sixth embodiment will be described with reference to FIG. FIG. 17 is a block diagram showing a configuration example of the information processing apparatus according to the sixth embodiment.

As shown in FIG. 17, in the information processing apparatus 10c, the storage unit 28b stores the history data 28D, and the control unit 30c includes the history data acquisition unit 58 and the learning unit 60. It is different from the information processing apparatus 10a shown in 1.

Since hobbies and tastes may differ from user to user, the value of the activity score may differ from user to user even if the same behavior is performed. The information processing apparatus 10c according to the sixth embodiment calculates an activity score using a trained model customized for each user.

The history data 28D is data related to the history of the activity score. The historical data 28D may include information regarding the ranking of activity scores for each user in a predetermined period. Specifically, the historical data 28D may include information on behavior patterns in which the activity score in a predetermined period is higher than a predetermined time. The predetermined period is, for example, 3 months, but is not limited to this.

18A to 18C are diagrams for explaining an example of the history data 28D. As shown in FIGS. 18A to 18C, in the history data 28D, the ranking, the behavior pattern, the behavior score, and the activity score are associated with each other. FIG. 18A is an example of the history data of the user U1. FIG. 18B is an example of the history data of the user U2. FIG. 18C is an example of the history data of the user U3. 18A to 18C show behavior patterns from users U1 to users U3 having high activity scores in a predetermined period, respectively, from 1st to 5th.

As shown in FIG. 18A, the first place of the user U1 is the behavior pattern MP4 having a behavior score of 10 and an activity score of 99. The second place of the user U1 is the behavior pattern MP3 having a behavior score of 9 and an activity score of 85. The third place of the user U1 is the behavior pattern MP1 having an behavior score of 8 and an activity score of 80. The fourth place of the user U1 is the behavior pattern MP9 having a behavior score of 7 and an activity score of 58. The fifth place of the user U1 is the behavior pattern MP3 having a behavior score of 7 and an activity score of 53.

As shown in FIG. 18B, the first place of the user U2 is the behavior pattern MP4 having an behavior score of 8 and an activity score of 90. The second place of the user U2 is the behavior pattern MP3 having an behavior score of 8 and an activity score of 88. The third place of the user U2 is the behavior pattern MP1 having an behavior score of 8 and an activity score of 79. The fourth place of the user U2 is the behavior pattern MP8 having an behavior score of 9 and an activity score of 51. The fifth place of the user U2 is the behavior pattern MP5 having an behavior score of 9 and an activity score of 49.

As shown in FIG. 18C, the first place of the user U3 is the behavior pattern MP7 having a behavior score of 10 and an activity score of 89. The second place of the user U3 is the behavior pattern MP2 having an behavior score of 6 and an activity score of 71. The third place of the user U3 is the behavior pattern MP9 having a behavior score of 7 and an activity score of 68. The fourth place of the user U3 is the behavior pattern MP4 having an behavior score of 8 and an activity score of 65. The fifth place of the user U3 is the behavior pattern MP3 having an behavior score of 9 and an activity score of 57.

As shown in FIGS. 18A to 18C, even if the behavior pattern is the same, the behavior pattern with a high activity score differs depending on the user, and there are individual differences in the activity score.

The history data acquisition unit 58 acquires the history data 28D from the storage unit 28c. Specifically, the history data acquisition unit 58 acquires the history data 28D of the user whose activity score is to be calculated.

The learning unit 60 generates a trained model for calculating a user's activity score by learning by machine learning based on learning data. In the present embodiment, the learning unit 60 generates, for example, a trained model for calculating the activity score based on the history data 28D acquired by the history data acquisition unit 58. The learning unit 60 learns the weight of DNN (Deep Neural Network) as a trained model for calculating the activity score, for example. The learning unit 60 may learn by using a well-known machine learning method such as deep learning. The learning unit 60 may update the trained model every time the learning data is updated, for example.

The learning process according to the sixth embodiment will be described with reference to FIG. FIG. 19 is a flowchart showing an example of the flow of the learning process according to the sixth embodiment.

The control unit 30c acquires learning data (step S110). Specifically, the history data acquisition unit 58 acquires the history data 28D for a predetermined period of the user for which the activity score is to be calculated from the storage unit 28b. The history data 28D acquired by the history data acquisition unit 58 may include at least information regarding a ranking, an action pattern, an action score, and an activity score. The history data acquisition unit 58 acquires, for example, the history data 28D from the 1st place to the 1000th place in the past 3 months.

The control unit 30c executes the learning process (step S111). Specifically, the learning unit 60 uses the history data 28D acquired by the history data acquisition unit 58 to learn and generate a trained model for calculating the user's activity score by machine learning. More specifically, the learning unit 60 uses a behavior pattern, a behavior score, an autonomic nerve activity degree, and an activity score as one data set, and a plurality of (for example, 1000) data sets as teacher data. Train to generate a trained model. The learning unit 60 generates, for example, a trained model for each user whose activity score is to be calculated. That is, in the present embodiment, a trained model customized for each user is generated.

The control unit 30c stores the trained model (step S112). Specifically, the learning unit 60 stores the generated learned model in the storage unit 28c.

[Processing content]
The processing content of the information processing apparatus 10c according to the sixth embodiment will be described with reference to FIG. 20. FIG. 20 is a flowchart showing an example of the processing flow of the information processing apparatus 10c according to the sixth embodiment.

Since the processes of steps S120 to S127 are the same as the processes of steps S40 to S47 shown in FIG. 9, the description thereof will be omitted.

The control unit 30c calculates the activity score based on the trained model according to the user (step S128). Specifically, the activity score calculation unit 54 calculates the user's activity score using a trained model customized according to the user.

Since the processes of steps S129 to S132 are the same as the processes of steps S49 to S52 shown in FIG. 9, the description thereof will be omitted.

As described above, the information processing apparatus 10c according to the sixth embodiment uses the trained model generated by customizing according to the history of the activity score for each user from the user U1 to the user U3, and uses the user U1. The activity score of the user U3 is calculated from. As a result, the information processing apparatus 10c according to the sixth embodiment can more appropriately calculate the activity score according to the user's sensibility.

[7th Embodiment]
The information processing system according to the seventh embodiment will be described with reference to FIG. 21. FIG. 21 is a diagram for explaining a configuration example of the information processing system according to the seventh embodiment.

As shown in FIG. 21, the information processing system 1 according to the seventh embodiment includes a plurality of information processing devices 10c and a server device 100. The information processing device 10c and the server device 100 are communicably connected to each other via a network N (for example, the Internet). That is, the information processing system 1 according to the seventh embodiment has a configuration in which the information processing device 10c according to the sixth embodiment and the server device 100 are communicably connected to each other.

In the above-mentioned sixth embodiment, the activity of the user is calculated using the trained model generated by customizing according to the history of the activity score for each user. In the seventh embodiment, the server device 100 stores the history data of a plurality of users as shared data, and has been learned based on the history data of the plurality of users whose activity score and the tendency of the behavior pattern are similar to each other. Generate a model to calculate user activity.

The configuration of the server device according to the seventh embodiment will be described with reference to FIG. 22. FIG. 22 is a block diagram showing a configuration example of the server device according to the seventh embodiment.

As shown in FIG. 22, the server device 100 includes a communication unit 110, a control unit 120, and a storage unit 130. The server device 100 is a so-called cloud server.

The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or a communication circuit. The communication unit 110 is connected to the network N wirelessly or by wire, and transmits / receives information to / from the information processing device 10c.

The control unit 120 controls the operation of each unit of the server device 100. The control unit 120 is realized by, for example, a CPU, an MPU, or the like executing a program stored in a storage unit (not shown) using a RAM or the like as a work area. The control unit 120 may be realized by an integrated circuit such as an ASIC or FPGA. The control unit 120 may be realized by a combination of hardware and software. The control unit 120 includes an acquisition unit 122, a determination unit 124, a request unit 126, and a provision unit 128.

The acquisition unit 122 acquires, for example, historical data regarding the history of the activity score of each user wearing the information processing apparatus 10c from the communication unit 110. The acquisition unit 122 acquires the history data 28D ₃ from the history data 28D ₁ of the user U3 from the user U1 shown in FIGS. 18A to 18C, for example. The acquisition unit 122 stores the acquired history data as shared data 132 in the storage unit 130.

The determination unit 124 determines the tendency of the history data among the shared data 132. The determination unit 124 determines, for example, whether or not there is a user whose tendency of historical data is similar.

The request unit 126 requests whether or not to allow other users to use their own history data when there are a plurality of users whose trends in the history data are similar to each other.

When the use of historical data is permitted, the providing unit 128 provides historical data to users whose trends in historical data are similar.

The storage unit 130 is a memory that stores various information such as calculation contents and programs of the control unit 120. For example, at least one of a RAM, a main storage device such as a ROM, and an external storage device such as an HDD. Including one.

Shared data 132 is stored in the storage unit 130. The shared data 132 may include historical data regarding activity scores of a plurality of users wearing the information processing apparatus 10c. The shared data 132 may include, for example, historical data 28D ₁ to historical data 28D ₃ from users U1 to users U3 shown in FIGS. 18A to 18C.

[Processing of server device]
A process of the server device according to the seventh embodiment will be described with reference to FIG. 23. FIG. 23 is a flowchart showing an example of the processing flow of the server device according to the seventh embodiment.

The control unit 120 refers to the shared data 132 and determines whether or not there is a user whose historical data is similar (step S140). For example, it is assumed that the shared data 132 includes the history data 28D ₁ to the history data 28D ₃ of the users U1 to U3 shown in FIGS. 18A to 18C. The determination unit 124, for example, history users who have the same behavior pattern from the first place to the third place, the behavior score of each behavior pattern is 8 or more, and the difference between the activity scores of each other is 10 or less. It is determined that the user has similar data. In this case, the determination unit 124 identifies that the first place of the activity score of the user U1 and the user U2 is the action pattern MP4, the second place is the action pattern MP3, and the third place is the action pattern MP1. The determination unit 124 identifies that the behavior score of the behavior pattern MP4 is 10, the behavior score of the behavior pattern MP3 is 9, and the behavior score of the behavior pattern MP1 is 8 for the user U1. The determination unit 124 identifies that the behavior score of the behavior pattern MP4 is 8, the behavior score of the behavior pattern MP3 is 8, and the behavior score of the behavior pattern MP1 is 8 for the user U2. The determination unit 124 has a difference in the activity score of the behavior pattern MP1 of 9, a difference in the activity score of the behavior pattern MP3 of 3, and a difference in the activity score of the behavior pattern MP1 for the user U1 and the user U2. Identify that it is 1. In this case, the determination unit 124 determines that the history data of the user U1 and the user U2 are similar to each other.

In the present embodiment, the determination unit 124 has described the case where two users are selected from the three users U1 to U3, but this is an example and is particularly limited to the number of actual user populations. There is no limit. The determination unit 124 may determine that the history data of three or more users are close to each other. The determination unit 124 may determine whether or not the historical data are close to each other by a method other than the method described in this embodiment. The determination unit 124 may determine, for example, whether or not the historical data are close to each other according to a predetermined conditional expression defined mathematically.

If it is determined that there is an approximate user (step S140; Yes), the process proceeds to step S141. When it is determined that there is no approximate user (step S140; No), the process of FIG. 23 is terminated.

If it is determined to be Yes in step S140, the control unit 120 requests the user whose historical data is close to each other for permission to share (step S141). Specifically, the request unit 126 transmits a notification requesting the user U1 and the user U2 for permission to share the historical data via the communication unit 110.

The control unit 120 determines whether or not the sharing request is permitted (step S142). Specifically, the request unit 126 determines whether or not there is a response from the user U1 or the user U2 to permit the sharing of the history data in response to the request for permission to share the history data transmitted in step S141. .. If it is determined that the sharing request is permitted (step S142; Yes), the process proceeds to step S143. When it is determined that the sharing request is not permitted (step S142; No), the process of FIG. 23 is terminated.

If it is determined to be Yes in step S142, the control unit 120 shares the history data (step S143). Specifically, the providing unit 128, for example, when the user U2 permits sharing of historical data, the user U1 with respect to the information processing device 10c worn by the user U1 via the communication unit 110. History data 28D ₁ is transmitted. Then, the process of FIG. 23 is completed.

The learning process according to the seventh embodiment will be described with reference to FIG. 24. FIG. 24 is a flowchart showing an example of the flow of the learning process according to the seventh embodiment. Hereinafter, a process in which the information processing device 10c worn by the user U1 acquires the history data 28D ₂ of the user U2 and performs the learning process will be described.

The control unit 30c acquires historical data (step S150). Specifically, the history data acquisition unit 58 acquires the history data 28D ₁ for a predetermined period of the user for which the activity score is to be calculated from the storage unit 28c. The history data 28D ₁ acquired by the history data acquisition unit 58 may include at least information regarding a ranking, an action pattern, an action score, and an activity score. The history data acquisition unit 58 receives, for example, the history data 28D ₁ from the first place to the 1000th place in the past three months. That is, the history data acquisition unit 58 acquires 1000 sets of data sets as teacher data.

The control unit 30c acquires history data that is close to the history data of the user U1 from the server device 100 (step S151). Specifically, the history data acquisition unit 58 acquires the history data 28D ₂ of the user U2 from the server device 100 via, for example, the communication unit 26. Here, for example, it can be assumed that there are a small number of behavior patterns included in 1000 sets of data _sets of historical data 28D1. For example, when generating a trained model, 6000 to 8000 sets or more of data sets may be required. In the present embodiment, the history data acquisition unit 58 acquires the history data 28D ₂ that is close to the history data 28D ₁ from the server device 100, so that the number of data sets can be supplemented and a more optimal trained model can be generated. ..

The control unit 30c executes the learning process (step S152). Specifically, the learning unit 60 learns a trained model for calculating a user's activity score by machine learning using the history data 28D ₁ and the history data 28D ₂ acquired by the history data acquisition unit 58. To generate.

The control unit 30c stores the trained model (step S153). Specifically, the learning unit 60 stores the generated learned model in the storage unit 28b. Then, the process of FIG. 24 is terminated.

[Processing content]
The processing content of the information processing apparatus 10c according to the seventh embodiment will be described with reference to FIG. 25. FIG. 25 is a flowchart showing an example of the processing flow of the information processing apparatus 10c according to the seventh embodiment.

Since the processes of steps S160 to S167 are the same as the processes of steps S120 to S127 shown in FIG. 20, the description thereof will be omitted.

The control unit 30c calculates the activity score based on the trained model generated using the shared data (step S168). Specifically, the activity score calculation unit 54 calculates the activity score of the user U1 by using the trained model generated by using the history data 28D ₁ and the history data 28D ₂ .

Since the processes of steps S169 to S172 are the same as the processes of steps S129 to S132 shown in FIG. 20, the description thereof will be omitted.

As described above, the information processing apparatus 10c according to the seventh embodiment generates a trained model using the history data 28D ₁ of the user U1 and the history data 28D ₂ of the user U2 which is close to the history data 28D ₁ . The activity score is calculated using the trained model. As a result, the information processing apparatus 10c according to the seventh embodiment can more appropriately calculate the activity score.

[Eighth Embodiment]
Next, the information processing apparatus according to the eighth embodiment will be described with reference to FIG. 26. FIG. 26 is a block diagram showing a configuration example of the information processing apparatus according to the eighth embodiment.

As shown in FIG. 26, the information processing apparatus 10d is different from the information processing apparatus 10 shown in FIG. 2 in that the output unit 24a includes the tactile stimulus output unit 24C.

Since the user's biometric information changes from moment to moment, the user's activity score may also change according to the change in the biometric information. The information processing apparatus 10d presents the change in the activity score in an easy-to-understand manner by relatively presenting the temporal transition of the activity score of the user.

The tactile stimulus output unit 26C is a device that outputs the tactile stimulus of the user U. For example, the tactile stimulus output unit 26C outputs a tactile stimulus to the user by physically operating such as vibration, but the type of the tactile stimulus is not limited to vibration or the like and may be arbitrary.

In the output control unit 50 of the control unit 30d of the information processing apparatus 10d according to the eighth embodiment, the output control unit 50 controls the output unit 24a, and the activity score calculation unit 54 calculates the activity score in time. The information showing the transition is output. In other words, the output control unit 50 controls the output unit 24a to output information indicating a relative change in the activity score.

Using FIGS. 27A to 27E, a method of relatively displaying the temporal transition of the activity score will be described. 27A to 27E are diagrams for explaining a method of relatively displaying the temporal transition of the activity score.

As shown in FIG. 27A, the output control unit 50 controls, for example, the display unit 24A to display the temporal transition of the activity score as the graph G1. In FIG. 27A, the horizontal axis represents time and the vertical axis represents activity score. In the graph G1, the time t0 indicates the time when the calculation of the activity score is started. By visually recognizing the graph G1, the user can easily grasp the temporal transition of the activity score.

As shown in FIG. 27B, the output control unit 50 controls, for example, the display unit 24A to display the value of the activity score at the start and the value of the current activity score in parallel. In the example shown in FIG. 27B, it is shown that the activity score at the start is 56 and the current activity score is 78. As a result, the user can easily grasp the transition of the activity score over time.

As shown in FIG. 27C, the output control unit 50 controls, for example, the display unit 24A to display an arc obtained by dividing a circle centered on the lower left corner of the screen into four equal parts. In the example shown in FIG. 27C, the arc C1 indicates the activity score at the start time, and the radius r1 indicates the magnitude of the activity score. In the example shown in FIG. 27C, arc C2 and arc C3 indicate the current activity score. If the current activity score is larger than the starting point, the output control unit 50 causes the display unit 24A to display the arc C2 having a radius r2 larger than the radius r1 together with the arc C1 having a radius r1. If the current activity score is smaller than the start time, the output control unit 50 causes the display unit 24A to display the arc C3 having a radius r3 smaller than the radius r1 together with the arc C1 having a radius r1. As a result, the user can easily grasp the transition of the activity score over time.

As shown in FIG. 27D, the output control unit 50 controls, for example, the display unit 24A to display a bar. In the example shown in FIG. 27D, the bar B1 is displayed in the central portion of the display unit 24A, but the present invention is not limited to this, and the bar B1 may be displayed in the lower left corner or in the lower right corner. In the example shown in FIG. 27D, the bar B1 indicates the activity score at the start time, and the height h1 indicates the magnitude of the activity score. In the example shown in FIG. 27D, bars B2 and B3 indicate the current activity score. If the current activity score is higher than the starting point, the output control unit 50 causes the display unit 24A to display the rod B2 having a height h2 higher than the height h1 together with the rod B1 having a height h1. If the current activity score is smaller than the starting point, the output control unit 50 causes the display unit 24A to display the rod B3 having a height h3 lower than the height h1 together with the rod B1 having a height h1. As a result, the user can easily grasp the transition of the activity score over time.

As shown in FIG. 27E, the output control unit 50 controls the display unit 24A, for example, to display the graph G2. Graph G2 can be a bar graph. In FIG. 27E, the horizontal axis represents time and the vertical axis represents activity score. In the graph G2, the time t0 indicates the time when the calculation of the activity score is started. By visually recognizing the graph G2, the user can easily grasp the temporal transition of the activity score.

The output control unit 50 may control, for example, the voice output unit 24B or the tactile stimulus output unit 26C to output information indicating the temporal transition of the activity score.

A method of outputting information indicating a temporal transition of the activity score by the voice output unit 24B or the tactile stimulus output unit 24C will be described with reference to FIG. 28. FIG. 28 is a diagram for explaining a method of outputting information indicating a temporal transition of the activity score by the voice output unit 24B or the tactile stimulus output unit 24C.

In FIG. 28, the horizontal axis represents time and the vertical axis represents activity score. The activity score NS1 at time t0 is the activity score at the time when the calculation of the activity score is started. In the example shown in FIG. 28, the activity score NS1 is used as a reference. The output control unit 50 sets, for example, the volume of the sound output from the sound output unit 24B indicating the activity score NS1 which is the reference. The output control unit 50 sets, for example, the strength of the stimulus (for example, vibration) to be output from the tactile stimulus output unit 24C showing the activity score NS1 as a reference.

Here, it is assumed that the activity score becomes the activity score NS2, which is larger than the activity score NS1 at the time t1. In this case, the output control unit 50 controls, for example, the voice output unit 24B to output the voice corresponding to the activity score NS1 and the voice corresponding to the activity score NS2 as a set. For example, the output control unit 50 controls the tactile stimulus output unit 24C to output a stimulus corresponding to the activity score NS1 and a stimulus corresponding to the activity score NS2 as a set. The voice corresponding to the activity score NS2 is louder than the voice corresponding to the activity score NS1. The stimulus corresponding to the activity score NS2 is stronger than the stimulus corresponding to the activity score NS1. The loudness of the voice corresponding to the activity score NS2 is preferably changed according to, for example, the ratio of the activity score NS2 to the activity score NS1. The intensity of the stimulus corresponding to the activity score NS2 is preferably changed according to, for example, the ratio of the activity score NS2 to the activity score NS1. Thereby, the user can grasp the relative magnitude of the activity score NS2 with respect to the activity score NS1.

Here, it is assumed that the activity score becomes the activity score NS3, which is smaller than the activity score NS1 at the time t2. In this case, the output control unit 50 controls, for example, the voice output unit 24B to output the voice corresponding to the activity score NS1 and the voice corresponding to the activity score NS3 as a set. For example, the output control unit 50 controls the tactile stimulus output unit 24C to output a stimulus corresponding to the activity score NS1 and a stimulus corresponding to the activity score NS3 as a set. The voice corresponding to the activity score NS3 is smaller than the voice corresponding to the activity score NS1. The stimulus corresponding to the activity score NS3 is weaker than the stimulus corresponding to the activity score NS1. The loudness of the voice corresponding to the activity score NS3 is preferably changed according to, for example, the ratio of the activity score NS2 to the activity score NS1. The intensity of the stimulus corresponding to the activity score NS3 is preferably changed according to, for example, the ratio of the activity score NS2 to the activity score NS1. Thereby, the user can grasp the relative magnitude of the activity score NS2 with respect to the activity score NS1.

As described above, the information processing apparatus 10d according to the eighth embodiment relatively presents the temporal transition of the user's activity score. As a result, the information processing apparatus 10d according to the eighth embodiment makes it easy to understand the temporal change of the activity score.

Although the embodiments of the present disclosure have been described above, the present disclosure is not limited by the contents of these embodiments. Further, the above-mentioned components include those that can be easily assumed by those skilled in the art, those that are substantially the same, that is, those in a so-called equal range. Furthermore, the components described above can be combined as appropriate. Further, various omissions, replacements or changes of the components can be made without departing from the gist of the above-described embodiment.

The information processing device, information processing method, and program of the present disclosure can be applied to a technique for analyzing user behavior.

10, 10a, 10b, 10c, 10d Information processing device 20 Behavioral state sensor 20A Camera 20B Microphone

20C GNSS receiver

20D Acceleration sensor

20E Gyro sensor 20F Optical sensor 20G Temperature sensor 20H Humidity sensor 22 Input unit 24 Output unit 24A Display unit 24B Audio Output unit 24C Tactile stimulus output unit 26,110 Communication unit 28,130 Storage unit

28A Learning model

28B Map data 28C, 28Ca Correction data 28D History data 30,30a, 30b, 30c, 30d, 120 Control unit 32 Biosensor 32A Pulse wave Sensor 40 Behavior state information acquisition unit 42 Behavior pattern information generation unit 44 Behavior score calculation unit 46 Behavior pattern specification unit 48 Memory control unit 50 Output control unit 52 Biometric information acquisition unit 54 Activity score calculation unit 56 Activity score correction unit 58 History Data acquisition unit 60 Learning unit 100 Server device 122 Acquisition unit 124 Judgment unit 126 Request unit 128 Providing unit

Claims

A behavioral state detection sensor that detects behavioral state information related to the user's behavioral state,
Based on the behavioral state information, behavioral pattern information is generated in a multidimensional space whose coordinate axes are at least the date and time, place, and time when the behavioral state is detected, and the density of the behavioral pattern information group in which the behavioral pattern information is collected. Behavior pattern information generator that groups by space that exceeds a predetermined density,
An action score calculation unit that calculates information about the size of the space including the action pattern information group as an action score, and
A behavior pattern specifying unit that specifies the behavior pattern information group in the space where the value of the behavior score is equal to or higher than a predetermined value as the behavior pattern of the user.
An information processing device equipped with.
The behavior pattern specifying unit determines the behavior pattern in which the value of the behavior score is larger than the first threshold value as an extraordinary behavior, and determines the behavior pattern in which the value is smaller than the second threshold value as a daily behavior.
The information processing apparatus according to claim 1.
The behavior pattern information generation unit generates the behavior pattern information by plotting points in the multidimensional space.
The information processing apparatus according to claim 1 or 2.
A biosensor that detects biometric information related to the user's biometric information,
The activity score calculation that calculates the autonomic nerve activity of the user based on the biological information and calculates the activity score of the user based on the autonomic nerve activity, the behavior score, and the behavior pattern. With a department,
The information processing apparatus according to any one of claims 1 to 3.
It is provided with an activity score correction unit that corrects the activity score based on the country or region in which the user's behavior pattern is specified.
The information processing apparatus according to claim 4.
The activity score calculation unit calculates the current activity score of the user based on the history of the activity score of the user.
The information processing apparatus according to claim 4 or 5.
It is provided with an output control unit that outputs the temporal transition of the activity score of the user by using the output unit.
The information processing apparatus according to any one of claims 4 to 6.
The current activity score of the user is calculated based on the history of another user whose behavior pattern and the activity score are similar to each other.
The information processing apparatus according to any one of claims 4 to 7.
A behavioral state sensor that detects behavioral state information related to the user's behavioral state,
A biosensor that detects biometric information related to the user's biometric information,
An autonomic nerve activity calculation unit that calculates the autonomic nerve activity of the user based on the biological information, and an autonomic nerve activity calculation unit.
An output control unit that changes the intensity of the output from the output unit according to the intensity of the autonomic nerve activity,
An information processing device equipped with.
The output control unit changes the intensity of the output from the output unit according to the temporal transition of the intensity of the autonomic nerve activity of the user.
The information processing apparatus according to claim 9.
The output unit has a tactile stimulus output unit.
The output control unit changes the vibration intensity of the tactile stimulus output unit according to the intensity of the autonomic nerve activity.
The information processing apparatus according to claim 9 or 10.
A behavioral state sensor that detects behavioral state information related to the user's behavioral state,
A biosensor that detects biometric information related to the user's biometric information,
An autonomic nerve activity calculation unit that calculates the autonomic nerve activity of the user based on the biological information, and an autonomic nerve activity calculation unit.
An autonomic nerve activity correction unit that corrects the autonomic nerve activity based on the country or region in which the user's behavior pattern is specified,
An information processing device equipped with.
The autonomic nerve activity correction unit corrects the autonomic nerve activity using a correction coefficient predetermined according to the country or region.
The information processing apparatus according to claim 12.
Steps to detect behavioral state information about the user's behavioral state,
Based on the behavioral state information, at least the behavioral pattern information is generated in a multidimensional space whose coordinate axes are the date and time, place, and time when the behavioral state is detected, and the density of the behavioral pattern information group in which the behavioral pattern information is collected is increased. Steps to group by space above a given density,
The action score calculation step of calculating the information about the size of the space including the action pattern information group as the action score, and the action score calculation step.
A step of specifying the behavior pattern information group in which the value of the behavior score is equal to or higher than a predetermined value as the behavior pattern of the user, and
Information processing methods, including.
Steps to detect behavioral state information about the user's behavioral state,
The step of detecting the biometric information regarding the biometric information of the user, and
A step of calculating the autonomic nervous activity of the user based on the biometric information,
The step of changing the intensity of the output from the output unit according to the intensity of the autonomic nerve activity, and
Information processing methods, including.
Steps to detect behavioral state information about the user's behavioral state,
The step of detecting the biometric information regarding the biometric information of the user, and
A step of calculating the autonomic nervous activity of the user based on the biometric information,
A step of correcting the autonomic nervous activity based on the country or region in which the user's behavior pattern is specified, and
Information processing methods, including.
Steps to detect behavioral state information about the user's behavioral state,
Based on the behavioral state information, at least the behavioral pattern information is generated in a multidimensional space whose coordinate axes are the date and time, place, and time when the behavioral state is detected, and the density of the behavioral pattern information group in which the behavioral pattern information is collected is increased. Steps to group by space above a given density,
The action score calculation step of calculating the information about the size of the space including the action pattern information group as the action score, and the action score calculation step.
A step of specifying the behavior pattern information group in which the value of the behavior score is equal to or higher than a predetermined value as the behavior pattern of the user, and
A program that lets your computer run.
Steps to detect behavioral state information about the user's behavioral state,
The step of detecting the biometric information regarding the biometric information of the user, and
A step of calculating the autonomic nervous activity of the user based on the biometric information,
The step of changing the intensity of the output from the output unit according to the intensity of the autonomic nerve activity, and
A program that lets your computer run.
Steps to detect behavioral state information about the user's behavioral state,
The step of detecting the biometric information regarding the biometric information of the user, and
A step of calculating the autonomic nervous activity of the user based on the biometric information,
A step of correcting the autonomic nervous activity based on the country or region in which the user's behavior pattern is specified, and
A program that lets your computer run.